The third variable could be one which is correlated to both variables. These are called confounding variable.
For example, in the UK you could find a correlation between coastal air pollution and ice cream sales. This is not because eating ice cream causes air pollution nor because air pollution causes people to eat ice cream. The confounding variable is the temperature. Warm weather gets people to drive to the sea!
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That depends on what you want to know, the form of the answer, how and where the survey is to be conducted and lots of other variables. Once you have all of that sorted, the best and simplest method is to consult tables or software.
The population is every data point you intend to generalise the survey results to. The sample frame is those data points that you can pick from for the survey. The sample is which of these data points you actually survey, and the sample size is how many of those data points there are. For instance, if you have 700 students in a school, and you have access to 300 of them, and decide to give 30 of them a survey, the sample size is 30.
Correlation only shows how well two variables vary together; it does not show the causation of the effect - there is often a third factor (or variable) which causes both, or causes one and influences the other.An example:A survey of all the inhabitants in a village found a strong (but not perfect) correlation between foot (shoe) size and mathematical ability - the larger the foot size the better they were at mathematics. Does this mean that foot size causes the mathematical ability of someone?In this case no; there is a third factor which causes the first (foot size) and has some influence over the second (mathematical ability): the survey was of all the inhabitants of the village which includes babies and young children.The third factor here (the real cause of the "apparent" correlation) is the age of the inhabitant: the older someone gets (from being a baby to a teenager) the larger their foot size will become, but also the better their mathematical ability is (likely) to get. The babies with the smaller feet will have very limited mathematical ability, the 7 year olds with larger feet will have better mathematical ability, the teenagers with even larger feet will have better mathematical ability again. The correlation need not be perfect as there will be older people with less mathematical ability, but "on average" (sic) the older someone is the better their mathematical ability (along with the larger their feet).
Data compilation is taking survey or evaluation answers, gathering them into a database, and analyzing the results for further suggestions, improvements, and/or recommendations.
affect the results of the survey.
Correlational surveys involve measuring the relationship between two or more variables without manipulating them. By collecting data on these variables from a sample of participants, researchers can determine the extent to which changes in one variable are associated with changes in another, providing insight into potential patterns or connections between the variables.
Yes, a survey typically includes variables that are measured or observed, such as demographics, opinions, behaviors, or attitudes. These variables help researchers analyze and interpret the data collected from the survey.
You can find the results of a customer satisfaction survey you have taken part in by contacting the company which provided the survey. Sometimes you can also find the results posted online.
The last step of taking a survey typically involves submitting your responses. This can be done by clicking a "Submit" or "Finish" button provided at the end of the survey. Make sure to review your answers before submitting to ensure accuracy.
The answer depends on what information the survey collects.
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A sample survey is quicker and cheaper. If the survey is well designed then the results are likely to be close to their true values.
when there are errors in sampling design, such as biases in selecting participants or a non-representative sample, which can lead to inaccurate results.
Data analysis must be used to understand the results of a survey. Otherwise, the data collected by the survey would remain a jumbled collection of data.
The answer will depend on what variable you want to calculate.
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